On Tuesday, I asked a question that might’ve sounded a bit strange at first:
What if your company had a theory of itself?
Not a narrative. Not a pitch.
But a real, structured understanding of what it is, how it behaves, and why it changes over time.
Today, I want to unpack what that really means—practically, philosophically, and technologically. Because I think it signals something new. Not just for how we analyze companies, but for how companies might begin to understand themselves.
Let’s dive in.
1. From KPIs to Causal Structure
Let’s start with the basics: most analytics tools today are descriptive.
They show you the what—revenue trends, churn rates, ESG scores, sentiment metrics. That’s useful, but limited.
At Wangari, we’re not trying to build a prettier dashboard.
We’re trying to build a causal model: a digital twin that mirrors the why.
We do this by constructing Directed Acyclic Graphs (DAGs)—structures where variables like team diversity, innovation output, or brand sentiment are linked by causal edges. These edges aren’t just statistical correlations. They’re hypotheses about how change propagates.
They allow us to simulate interventions—what happens if this variable changes?—and generate testable insights.
This is critical.
Because in a complex system, the structure is the story.
2. The Company as a Living System
Let’s zoom out.
At Wangari, we think of the company not just as a legal entity or financial vehicle—but as a living, adaptive system.
It has internal dynamics: structure, norms, incentives, hierarchies, feedback loops.
And it exists within an external environment: markets, competitors, regulators, even culture and public perception.
Our models aim to bridge the two.
They don’t just track performance—they help explain how the company behaves as a system.
That means recognizing elements like:
Cultural patterns (e.g. trust, adaptability, psychological safety),
Strategic intent (e.g. goal-setting, innovation rhythms),
Environmental context (e.g. policy shifts, media sentiment, macro shocks).
And then modeling how these influence each other over time.
Not all data matters. But the relationships between the right variables? That’s where the leverage lies.
3. Awareness as a Competitive Advantage
Now here’s where things get interesting.
When an organization starts engaging with a causal model of itself—when it starts asking questions like “What drives our resilience?” or “How does leadership structure affect adaptability?”—something happens.
It begins to become aware of itself.
We often talk about AI gaining awareness or consciousness, especially with the rise of large language models.
But here’s a provocative twist:
What if the real shift isn’t machines becoming conscious—
but organizations, through AI, becoming more self-aware?
A self-aware organization doesn’t just chase metrics.
It reflects. It learns. It adapts strategically.
It builds feedback loops. It asks better questions.
It doesn’t just act—it understands its own actions in context.
In that sense, a good causal model becomes a kind of mirror—or even, as I sometimes like to say, a glass brain.
It lets the company “see itself seeing.” That’s powerful.
4. Simulating Futures, Not Just Reporting the Past
Most tools show you the past. Wangari is built to explore possibility space.
By embedding causal structure into a dynamic digital twin, we allow for scenario generation:
What if we restructured incentives in R&D?
What if our supply chain gets hit by a policy shock?
What if female representation in leadership increases by 30%?
These are more than hypothetical thought experiments.
They’re simulated interventions—based on live data and informed by statistical and expert priors.
The point isn’t to be perfectly predictive.
The point is to help organizations think more like scientists.
To test hypotheses, see knock-on effects, and compare paths—not just pick KPIs and hope for the best.
5. The Future of Strategic Intelligence
So what does this mean for the future?
I think we’re moving from dashboard logic to model logic.
From what’s happening? to why is it happening—and what if we changed it?
Wangari isn’t just a tool for ESG analysts or sustainability officers.
It’s the beginning of a new kind of strategic intelligence platform.
One that helps leaders, asset managers, policymakers—even founders—build a theory of the system they’re working with.
In a world that’s volatile, interconnected, and increasingly AI-mediated, this kind of clarity might be the most valuable asset of all.
Final Thought: Seeing Yourself Is Power
To close, I’ll share something personal.
As someone who started out in theoretical physics, I’ve always been fascinated by invisible forces—by what’s beneath what we observe.
I think organizations are the same.
They behave in complex, emergent ways. But underneath, there are patterns—causal structures—that shape outcomes.
And if we can map those, model them, and reflect them back to the people running the company…
Then we’re doing something really meaningful.
We’re giving organizations a chance to see themselves clearly.
And once they do that, they can evolve with intention—not just react to noise.
Thanks for listening.
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